x <- rbinom(1000, 8, 0.5)
hist(x)
sum(x<3)/1000
## [1] 0.152
x[1:20]
## [1] 2 3 3 4 5 3 3 4 7 4 5 4 3 4 2 4 3 2 4 6
library(rjags)
## Warning: package 'rjags' was built under R version 3.4.3
## Loading required package: coda
## Warning: package 'coda' was built under R version 3.4.2
## Linked to JAGS 4.3.0
## Loaded modules: basemod,bugs
#Model is defined as a string
modelclass.bug <- "model {Y ~ dbin(0.5,8)
P2 <- step(2.5-Y)
}"
modelclass_11 <- jags.model(textConnection(modelclass.bug))
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 0
## Unobserved stochastic nodes: 1
## Total graph size: 6
##
## Initializing model
#Gibbs sampling for 1000 iterations
mcmc <- update(modelclass_11, n.iter = 1000, progress.bar = "gui")
test <- coda.samples(modelclass_11, c("P2" , "Y"), n.iter = 10000)
summary(test)
##
## Iterations = 1001:11000
## Thinning interval = 1
## Number of chains = 1
## Sample size per chain = 10000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## P2 0.1451 0.3522 0.003522 0.003522
## Y 3.9961 1.4104 0.014104 0.014104
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## P2 0 0 0 0 1
## Y 1 3 4 5 7
plot(test)
